data-analysis

# Data Analysis Skill Process data files in the `data/` directory, perform analysis, and output reports to `reports/`. ## Step-by-Step Workflow 1. **Identify the data source** — List available files and confirm with the user which to analyze: ```bash ls data/ ``` 2. **Load and inspect the data** — Use Python to read the file and show a summary: ```python import pandas as pd df = pd.read_csv("data/sales.csv") # or read_excel / read_json print(f"Shape: {df.shape}") print(f"Columns: {list(df.columns)}") print(df.dtypes) print(df.describe()) print(f"Missing values:\n{df.isnull().sum()}") ``` 3. **Clean the data** — Handle missing values, fix types, remove duplicates: ```python df = df.drop_duplicates() df["date"] = pd.to_datetime(df["date"], errors="coerce") df["amount"] = pd.to_numeric(df["amount"], errors="coerce") df = df.dropna(subset=["date", "amount"]) print(f"Clean shape: {df.shape}") ``` 4. **Analyze** — Compute the requested statistics or aggregations: ```python # Example: monthly revenue trend monthly = df.groupby(df["date"].dt.to_period("M"))["amount"].sum() print(monthly) # Example: correlation matrix print(df[["amount", "quantity", "discount"]].corr()) ``` 5. **Visualize** — Generate charts and save to `reports/`: ```python import matplotlib matplotlib.use("Agg") import matplotlib.pyplot as plt monthly.plot(kind="bar", title="Monthly Revenue") plt.tight_layout() plt.savefig("reports/monthly_revenue.png", dpi=150) plt.close() print("Chart saved to repo...